Quality of user experience analysis

ABSTRACT

The techniques described herein involve analysis of communication data included in trace file(s) of device(s) involved in a communication. These trace file(s) may each include data associated with multiple layers of a communication protocol stack of a respective device or data associated with a single such layer. The techniques may further involve one or more of determination of performance metrics associated with data at a specific layer of a specific device, correlation of the data between layers of a device, or correlation of data across multiple device(s) involved in the communication. The performance metrics or correlated data may then be analyzed based on thresholds or models to determine whether the performance metrics or correlated data exhibits a degraded quality of user experience. Also or instead, graphic or textual representations of the performance metrics or correlated data may be generated.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of co-pending U.S. patentapplication Ser. No. 15/235,987 filed Aug. 12, 2016, which is acontinuation of U.S. patent application Ser. No. 14/183,300 filed Feb.18, 2014, now U.S. Pat. No. 9,538,409, which is a continuation-in-partof U.S. patent application Ser. No. 13/738,799, filed on Jan. 10, 2013,now U.S. Pat. No. 9,237,474, which claims priority filing benefit fromU.S. Provisional Application No. 61/719,929, filed Oct. 29, 2012, whichare incorporated herein by reference in their entirety.

BACKGROUND

Modern telecommunication systems include heterogeneous mixtures ofsecond, third, and fourth generation (2G, 3G, and 4G) cellular-wirelessaccess technologies, which may be cross-compatible and may operatecollectively to provide data communication services. Global Systems forMobile (GSM) is an example of 2G telecommunications technologies;Universal Mobile Telecommunications System (UMTS) is an example of 3Gtelecommunications technologies; and Long Term Evolution (LTE),including LTE Advanced, and Evolved High-Speed Packet Access (HSPA+) areexamples of 4G telecommunications technologies.

The infrastructure that makes up the modern telecommunications networkscomprises multiple different components or devices (herein referred toas nodes) that are configured to generate, transmit, receive, relay,and/or route data packets so that data services can be requested by, andprovided to, user equipment (UE) subscribed to a plan offered by one ormore service providers or network communication providers that implementthe telecommunications networks.

However, the data services and/or data communications provided via thenodes may often experience problems causing service degradation due tothe vast amount of users and UEs accessing and requesting data via thetelecommunications networks. For example, problems causing servicedegradation may be associated with data traffic congestion due to a hightransfer demand for digital content (i.e., data transfer overload), andthis may lead to data packet loss, packet queuing delay, an inability toestablish a connection and other data communication and connectionproblems. These problems, if not addressed by a service provider or anetwork communication provider, degrade a network's Quality of Service(QoS) and an end user's Quality of User Experience (QoE) at the UE.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingfigures, in which the left-most digit of a reference number identifiesthe figure in which the reference number first appears. The use of thesame reference numbers in different figures indicates similar oridentical items or features.

FIG. 1A depicts an example environment where trace files can becollected from a plurality of nodes and correlated to identify networkoptimization opportunities, in accordance with embodiments of thedisclosure.

FIG. 1B depicts an example of part of an architecture of a QoEoptimization system, in accordance with embodiments of the disclosure.

FIG. 2 depicts example components of a client device configured toinitiate data communications and log trace file entries in a trace file,in accordance with embodiments of the disclosure.

FIG. 3A depicts an example data packet that may be logged in a tracefile, in accordance with embodiments of the disclosure.

FIG. 3B depicts an example trace file, in accordance with embodiments ofthe disclosure.

FIG. 4 depicts example components of a device configured to collect andcorrelate the trace files, as well as perform network analysis, inaccordance with embodiments of the disclosure.

FIG. 5 is an example data packet communication diagram that istransmitted over a network and that represents horizontal correlation,in accordance with embodiments of the disclosure.

FIG. 6 is an example model that represents vertical correlation, inaccordance with embodiments of the disclosure.

FIG. 7 is a flow chart of an example process for logging trace entriesin a trace file, in accordance with embodiments of the disclosure.

FIG. 8 is a flow chart of an example process for collecting andcorrelating the trace files so that network analysis can be performed,in accordance with embodiments of the disclosure.

FIG. 9 is a flow chart of another example process for collecting andcorrelating the trace files so that network analysis can be performed,in accordance with embodiments of the disclosure.

FIG. 10 shows a flow diagram of another example process for receiving atrace file, determining performance metrics for data included in thetrace file, and generating graphic or textual representations of theperformance metrics.

FIG. 11 shows a flow diagram of another example process for receivingtrace file(s), correlating trace file data associated with differentlayers of a device or with different devices, analyzing the correlateddata based on thresholds or models, and determining that communicationassociated with the correlated data exhibits a reduced QoE.

FIG. 12 is an example of a graphic representation of performance metricsassociated with communication engaged in by a device.

FIG. 13 is an example of a graphic representation of performance metricsassociated with communication engaged in by a device.

FIG. 14 is an example of a graphic representation of performance metricsassociated with communication engaged in by a device.

FIG. 15 is an example of a graphic representation of performance metricsassociated with communication engaged in by a device.

FIG. 16 is an example of a graphic representation of performance metricsassociated with communication engaged in by a device.

FIG. 17 is an example of a textual representation of performance metricsassociated with communication engaged in by a device.

DETAILED DESCRIPTION

The techniques described herein present opportunities for serviceproviders and/or network providers to optimize the QoE for data servicesby determining, using a broader network-based approach, the root causeof problems causing a service degradation (e.g., what problem isoccurring, why the problem is occurring, where in the telecommunicationsnetwork the problem is occurring). To determine the root cause of theproblems, the techniques may collect different trace files from multipledifferent nodes in the telecommunications network (or from acommunication interface between two nodes in the telecommunicationsnetwork) or from multiple or single layers of a communication protocolstack of one of the devices. Each trace file includes a log ofidentifications for numerous different data packets that have beengenerated, received, transmitted, relayed, and/or routed via the node inthe telecommunications network, and each trace file log entry may beassociated with a timestamp. Once collected, the techniques maycorrelate the different trace files from the multiple different nodes toidentify, using a broader network-based analysis, service optimizationopportunities. Also or instead, the techniques may correlate data fromdifferent layers of a communication protocol stack of one of thedevices, or may simply determine performance metrics for data from aspecific layer of a specific device. For example, after correlating thetrace files and determining that QoE has experienced a certain level ofdegradation, the techniques may provide an alert notification and arecommendation for optimization so that remedial actions may beimplemented to address the root cause of the problems.

In various embodiments, the techniques provide the alert notificationand recommendation to a network administrator when the trace filecorrelation and analysis determines that a key performance indicator(KPI) is not satisfying a minimum service level or service goalassociated with QoE. The network administrator may then initiate theremedial actions. In alternative embodiments, the collection of thetrace files, the correlation and analysis of the traces files and theimplementation of the remedial actions may be performed automaticallyvia a preset network configuration when service levels or service goalsare not being satisfied.

FIG. 1A depicts an illustrative environment 100 for collecting multipletrace files from different nodes that exchange data packets using atelecommunications network. To this end, the environment 100 may includea client device 102 (considered as a node herein), a mobiletelecommunications network (MTN) 104 that includes multiple MTN nodes106(1) . . . 106(N), one or more data servers 108, and a Quality ofExperience (QoE) optimization system 110. Moreover, the environment 100illustrates trace files that are logged at each node. For example, theclient device 102 is associated with one or more client device nodetrace files 112, and the MTN nodes 106(1) . . . 106(N) are eachassociated with one or more MTN node trace files 114(1) . . . 114(N). Invarious embodiments, the data servers 108 may each be associated withone or more data server node trace files 116.

The client device 102 may also be referred to as a UE, as mentionedabove. Thus, client devices 102 may include, but are not limited to,smart phones, mobile phones, cell phones, tablet computers, portablecomputers, laptop computer, personal digital assistants (PDAs), anelectronic book device, a handheld gaming unit, a personal media playerdevice, or any other portable electronic device that may generate voiceand/or digital data, request voice and/or digital data over the MTN 104,receive voice and/or digital data over the MTN 104, and/or exchangevoice and/or digital data over the MTN 104.

The MTN 104 may be configured to implement one or more of the second,third, and fourth generation (2G, 3G, and 4G) cellular-wireless accesstechnologies discussed above. Thus, the MTN 104 may implement GSM, UMTS,and/or LTE/LTE Advanced telecommunications technologies. Different typesof MTN nodes 106(1) . . . 106(N) in the GSM, UMTS, LTE, LTE Advanced,and/or HSPA+ telecommunications technologies may include, but are notlimited to, a combination of: base transceiver stations BTSs (e.g.,NodeBs, Enhanced-NodeBs), Radio Network Controllers (RNCs), serving GPRSsupport nodes (SGSNs), gateway GPRS support nodes (GGSNs), proxies, amobile switching center (MSC), a mobility management entity (MME), aserving gateway (SGW), a packet data network (PDN) gateway (PGW), anevolved packet data gateway (e-PDG), or any other data traffic controlentity configured to communicate and/or route data packets between theclient device 102 and the data servers 108. The MTN nodes 106(1) . . .106(N) may be configured with hardware and software that generatesand/or logs an entry in the MTN node trace files 114(1) . . . 114(N).While FIG. 1A illustrates an MTN 104, it is understood in the context ofthis document, that the techniques discussed herein may also beimplemented in other networking technologies, such as nodes that arepart of a wide area network (WAN), metropolitan area network (MAN),local area network (LAN), neighborhood area network (NAN), personal areanetwork (PAN), or the like.

In various embodiments, each trace entry includes an identificationassociated with a data packet that is communicated through an interfacefor the MTN nodes 106(1) . . . 106(N) or associated with a data packetrouted by the MTN nodes 106(1) . . . 106(N), as further discussedherein. In various embodiments, some of the MTN nodes 106(1) . . .106(N) may be part of a core network (e.g., backhaul portion, carrierEthernet) that is configured to access an IP-based network that providesdata communications services (e.g., so that clients can accessinformation at data servers 108). The data servers 108 may be ownedand/or operated by web-based content providers, including, but notlimited to: Bing®, Facebook®, Twitter®, Netflix®, Hulu®, YouTube®,Pandora®, iTunes®, Google Play®, Amazon Store®, CNN®, ESPN®, and thelike.

In various embodiments, the MTN 104 may be configured to exchange datapackets between the client device 102 and the data servers 108 usingwired and/or wireless links. Moreover, the MTN 104 may be configured todetermine a communications path or “pipe” so that the data packets canbe routed and exchanged accordingly.

The data services and data access applications discussed in thisdocument may include, but are not limited to, web browsing, videostreaming, video conferencing, network gaming, social mediaapplications, or any application or setting on the client device 102that is configured to generate and exchange data with data servers 108over the MTN 104.

In various embodiments, the QoE optimization system 110 may beconfigured to monitor and determine whether KPIs for the different dataservices are being satisfied or not satisfied in association with aparticular service level or service goal (e.g., a threshold or model),which may affect the QoE. Examples of KPIs for web browsing, as well asother applications executing on the client device 102, may includewebpage loading time, Domain Name System (DNS) lookup time, TransmissionControl Protocol (TCP) connect time, TCP round trip time (RTT),Hypertext Transfer Protocol (HTTP) response time, and so forth. Examplesof KPIs for video streaming and video conferencing, as well as otherapplications executing on the client device 102, may include applicationstart delays, catalog browsing, searching delay, video start delay, fastforward and rewind delay, a number of buffering events, duration perbuffering event, rebuffering ratio, a video frame rate, and so forth.Other KPIs for a UE may include application layer KPIs (such asaverage/minimum/maximum bit rate, traffic burstiness, amount of databytes transferred), transport layer KPIs (such as transmission controlprotocol (TCP) retransmissions and TCP resets), radio layer KPIs (suchas radio link control (RLC) retransmissions and RLC round trip time(RTT)), and physical layer KPIs (such as physical retransmissions,physical RTT, physical uplink (UL) interference, UE power, RACH time).The KPIs provided above are presented as examples, and thus, the list isnot exhaustive. Rather, service providers and/or network providers maycontemplate a large number of different KPIs which aid in gauging theQoE associated with the data services provided.

FIG. 1B depicts an example of part of an architecture 150 of a QoEoptimization system 110, in accordance with embodiments of thedisclosure. As illustrated, a QoE analyzer 152 of the architecture 150may receive trace file(s) 154(1), 154(2) . . . 154(J). The QoE analyzer152 may determinate performance metrics associated with KPIs 156 fordata from all or a subset of the trace file(s) 154(1), 154(2), . . .154(J). The QoE analyzer 152 may also correlate the data from the tracefile(s) 154(1), 154(2) . . . 154(J) and analyze the correlated databased on performance thresholds or performance models 158 to determinewhether communication represented by the trace file(s) 154(1), 154(2) .. . 154(J) exhibits a degraded QoE. The performance metrics orcorrelated data produced by the QoE analyzer 152 may then be used togenerate one or more graphic representations 160 and/or one or moretextual representations 162. Alternatively or additionally, an alert 164may be provided when the QoE analyzer 152 determines that thecommunication represented by the trace file(s) 154(1), 154(2) . . .154(J) exhibits a degraded QoE.

In various embodiments, the trace file(s) 154(1), 154(2) . . . 154(J)may be trace files from a single node (e.g., trace files 112, 114, or116) or may be trace files from multiple nodes (e.g., multiple ones oftrace files 112, 114, or 116). Each trace file 154 may include data froma single layer of a communication protocol stack (e.g., communicationprotocol stack 222) of a device (e.g., one of the client device 102, MTNnode 106, or data server 108) or from multiple layers of such a device.For example, trace files 154 may include transmission control protocol(TCP) logs, packet capture (PCAP) logs, Qualcomm eXtensible DiagnosticModule (QXDM) logs, application logs (e.g., LogCat logs), etc. The dataincluded in the trace file 154 may be associated with any sort ofcommunication such as a wireless communication, a wireless packet-basedcommunication, etc. Examples of such communications are describedfurther herein.

Data may be extracted from the trace files by an automated log parsertool, which may be associated, for example, with a trace file receivingmodule 410. The trace files 154 and/or the data extracted may then bestored in a trace file database 412 of the QoE optimization system 110.In some embodiments, the trace file receiving module 410 or anothermodule of the QoE optimization system 110 may then provide the dataextracted from the trace files 154 and/or the trace files 154 themselvesto the QoE analyzer 152.

The QoE analyzer 152 may be implemented by one or more modules of theQoE optimization system 110, such as the trace file correlation module414, the cross file analysis module 416, and the trace sorting module422. In some embodiments, the QoE analyzer 152 may retrieve dataassociated with a single layer (e.g., the radio layer) which wasincluded in the trace file 154 of a single device. Such data may beretrieved, for instance, from a trace file database 412 or may beprovided to the QoE analyzer 152 by the trace file receiving module 410.

The QoE analyzer 152 may then determine performance metrics associatedwith KPIs for the received/retrieved data. When the received/retrieveddata is associated with the radio layer, the QoE analyzer 152 maydetermine performance metrics associated with radio layer KPIs, such asRLC retransmissions, packet loss, network signaling, radio resourcecontrol (RRC) state duration, radio state transition times, times spentin different radio states, number of radio state transitions, orreconfiguration response times. When the received/retrieved data isassociated with a network, transport, or Internet layer, the QoEanalyzer 152 may determine performance metrics associated with KPIs suchas domain name service (DNS) RTT, TCP RTT, hypertext transfer protocol(HTTP) RTT, TCP retransmissions, TCP duplicate acknowledgements, TCPresets, TCP failures, delta frames, or sequence numbers. The QoEanalyzer 152 may then provide the determined performance metrics andindication of their associated KPIs to another module of the QoEoptimization system 110, such as the presentation and notificationmodule 424. That other module may then generate one or both of a graphicrepresentation 160 for some or all of the performance metrics or atextual representation 162 for some or all of the performance metrics.

FIGS. 12-16 are examples of graphic representations 160 of theperformance metrics determined by the QoE analyzer 152. In FIG. 12, thegraphic representation 160 is a radio state summary diagram. In FIG. 13,the graphic representation 160 is a graph of search keystroke HTTPresponse time(s). In FIG. 14, the graphic representation 160 is a graphof components of search keystroke HTTP response time(s). In FIG. 15, thegraphic representation 160 is a graph of the correlation of searchkeystroke response times with radio states. In FIG. 16, the graphicrepresentation 160 is a graph of the correlation of HTTP keystroke HTTPresponse times with radio states. Any number of other charts anddiagrams for performance metrics or correlated data associated with KPIs156 may also or instead be generated.

FIG. 17 is an example of a textual representation 162 of performancemetrics determined by the QoE analyzer 152. In FIG. 17, the textualrepresentation 162 is a radio state transition log. Any number of othertextual or log representations for performance metrics or correlateddata associated with KPIs 156 may also or instead be generated.

Returning to FIG. 1B, the QoE Analyzer 152 may also or instead retrievedata associated with multiple layers (e.g., the radio layer and thenetwork layer) which was included in one or more trace files 154 of asingle device. Such data may be retrieved, for instance, from a tracefile database 412 or may be provided to the QoE analyzer 152 by thetrace file receiving module 410. The QoE Analyzer 152 may then correlatereceived/retrieved data from different ones of the layers with eachother. The data being correlated may, for instance, represent a datapacket. The QoE Analyzer 152 may correlate data from a first layer whichrepresents the data packet with data from a second layer whichrepresents the data packet. In some embodiments, the QoE Analyzer 152may correlate the data based on the representations of the IP payload ofthe data packet in the first and second layers. As mentioned above, thecorrelation by the QoE Analyzer 152 may be implemented by a module ofthe QoE optimization system 110, such as the trace file correlationmodule 414. Correlation between layers is described below in furtherdetail with reference to FIG. 6.

In some embodiments, the QoE Analyzer 152 may also or instead retrievedata from multiple trace files 154 of multiple devices. Such data may beretrieved, for instance, from a trace file database 412 or may beprovided to the QoE analyzer 152 by the trace file receiving module 410.The QoE Analyzer 152 may then correlate the data. The data may becorrelated based on trace identifications (trace ID). Each device mayuse the same trace ID for the same data packet, request/response pair,or communication session. The correlation between trace files 154 ofmultiple devices by the QoE Analyzer 152 may be implemented by a moduleof the QoE optimization system 110, such as the trace file correlationmodule 414. This correlation is described further herein in greaterdetail.

In various embodiments, the QoE Analyzer 152 may then analyze thecorrelated data based on either or both of performance threshold ormodels 158. The performance thresholds or models 158 may be static orlearned. For example, the performance threshold or models 158 mayrepresent the typical communication of a data packet, arequest/response, or a session. When the correlated data does not matchor is outside of a tolerance threshold from the performance threshold ormodels 158, the QoE Analyzer 152 may determine that the communicationrepresented by the correlated data exhibits a reduced QoE. This analysisof correlated data may be implemented by a module of the QoEoptimization system 110, such as the cross file analysis module 416.This analysis is described further herein in greater detail.

When the QoE Analyzer 152 determines that the communication representedby the correlated data exhibits a reduced QoE, a module of the QoEoptimization system 110 may provide an alert 164 of the reduced QoE. Thepresentation and notification module 424 may be an example of such amodule and may provide alerts of reduced QoE responsive to determinationof the reduced QoE by the QoE analyzer 152.

Additionally or alternatively, the module of the QoE optimization system110, such as the presentation and notification module 424, may generatea graphic representation 160 or textual representation 162 for thecorrelated data.

FIG. 2 illustrates example components of the client device 102, which isconfigured to wirelessly transmit a request for data to the MTN 104 orreceive data from the data servers 108 over the MTN 104. Thus, theclient device 102 may include one or more processor(s) 202, a radiotransceiver 204 for wirelessly communicating with the MTN 104, and amemory 206 storing a device operating system (OS) 208, various softwareapplications 210 configured to request/receive data over the MTN 104, anetwork interface module 212, and the client device node trace files112.

In various embodiments, the applications 210 stored at the client device102 may include, but are not limited, a web browser application 214, avideo streaming application 216, an online gaming application 218, andso on, through an Nth software application 220. During execution on theclient device 102, each of the applications 210 may be configured tocause the client device 102 to initiate data communications with thedata servers 108 over the MTN 104.

The client device 102 may be configured to communicate over atelecommunications network using any common wireless and/or wirednetwork access technology. Moreover, the client device 102 may beconfigured to run any compatible device OS, including but not limitedto, Microsoft Windows Mobile®, Google Android®, Apple iOS®, LinuxMobile®, as well as any other common mobile device OS.

Each of the one or more processor(s) 202, can include one or morecentral processing units (CPUs) having multiple arithmetic logic units(ALUs) that perform arithmetic and logical operations, as well as one ormore control units (CUs) that extract instructions and stored contentfrom processor cache-level memory, and then executes instructions bycalling on the ALUs during program execution. In an implementation, theprocessor(s) 202 may be configured to execute each of the softwareapplications 210 stored in the memory 206. In various embodiments, thenetwork interface module 212 may be configured to detect an action(e.g., operation, command, user input) directed to one of theapplications 210, the action triggering the generation of a datatransfer request and a transmission of the data transfer request.

The memory 206 may be implemented using computer readable media, such ascomputer storage media. Computer-readable media includes, at least, twotypes of computer-readable media, namely computer storage media andcommunications media. Computer storage media includes volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules, or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other non-transmission medium that can be used to storeinformation for access by a computing device. In contrast, communicationmedia may embody computer readable instructions, data structures,program modules, or other data in a modulated data signal, such as acarrier wave, or other transmission mechanism.

In various embodiments, the client device node trace files 112 maycorrespond to individual ones of multiple layers of a communicationprotocol stack 222 associated with the network interface module 212 ofthe client device 102. For example, the multiple layers of thecommunication protocol stack 222 may correspond to the Open SystemsInterconnection (OSI) model characterizing and standardizing functionsof a communications system in terms of abstraction layers. The multiplelayers may also correspond to the Internet Protocol (IP) suite. Forexample, in various embodiments, the client device 102 may log a singleclient device node trace file 112 for each of a physical layer, a datalink/radio layer, a network layer/Internet layer, a transport layer, asession layer, a presentation layer, and an application layer, as a datapacket is generated and configured amongst the layers for communicationfrom the client device 102 to the data servers 108 over the MTN 104.

Moreover, the client device 102 may log a single client device nodetrace file 112 for a particular set of the layers of the communicationprotocol stack 212. For example, the client device 102 may log a firstclient device node trace file 112 for theapplication/presentation/session layers, a second client device nodetrace file 112 for the transport/network layers, a third client devicenode trace file 112 for the data link layer, and a fourth client devicenode trace file 112 for the physical layer. By logging trace files atthe layer level of the client device 102, the QoE optimization system110 may be able to determine the root cause of problems at a moregranular level after collecting the trace files at the layer level (ascompared to the node level). This may further help when identifyingremedial actions that optimize the QoE.

Similar to the multiple different layers at the client device 102, eachof the MTN nodes 106(1) . . . 106(N), as well as each of the dataservers 108, may also log different trace files (e.g., 114(1) . . .114(N) and 116) for individual layers, or defined combination(s) oflayers of the communication protocol stack of that MTN node 106/dataserver 108. Accordingly, the QoE optimization system 110 may alsoidentify the root cause of problems at a more granular level at the MTNnodes 106(1) . . . 106(N) and the data servers 108.

FIG. 3A depicts an example data packet 300 configured to be logged inone of the client device node trace files 112, the MTN node trace files114(1) . . . 114(N), or the data server node trace files 116. The datapacket 300 may be configured in association with one or morecommunication or data exchange/formatting protocols such as TCP, IP,HTTP or other protocols directed to communicating or exchanging contentover the MTN 104.

In various embodiments, the data packet 300 may include a header portion302 and a payload portion 304. The data packet may further include aportion including N fields, at least a portion of which are used tocreate a trace ID 306 for the data packet. In various embodiments, thefields used to create the trace ID 306 may be part of the header portion302, the payload portion 304, or a combination thereof.

In various embodiments, one or more of the N fields may be associatedwith routing and addressing information commonly included in the datapacket, or one of more fields that may be defined and are unique to aparticular protocol. For example, a field may include a Packet DataProtocol (PDP) address, a source port number, a destination port number,a checksum number (for IPv4 or IPv6), a sequence number, anacknowledgement number, an Internet Protocol (IP) address, a sourceaddress, a destination address or any other field in the data packetthat may help distinguish one data packet from another. Moreover, afield may also help identify a request/response sequence or pair, or aparticular communication session established, such that data packets canbe matched and/or correlated correctly, even though the trace ID 306 asa whole may not be an exact match.

Accordingly, the trace ID 306 may be comprised of a single field, or acombination of two fields, three fields, four fields, and so forth. Themore fields used to comprise the trace ID 306 may help ensure that thetrace ID 306 is unique for the data packet or correlates related datapackets, so that the data packets can be tracked through theircommunication paths. In at least one embodiment, the trace ID 306includes four fields: a PDP address, a checksum number, a source portnumber, and a destination port number.

FIG. 3B depicts an example trace file 308 that may correspond to theclient device node trace files 112 logged at the client device, the MTNnode trace files 114(1) . . . 114(N) logged at the MTN nodes 106(1) . .. 106(N), or the data server node trace files 116 logged at the dataservers 108. The trace file 308 may include a node identifier 310 thatthe QoE optimization system 110 may use so that it knows what node(e.g., the client device 102, one of the MTN nodes 106(1) . . . 106(N),or a data server 108) the trace file is associated with after the QoEoptimization system 110 collects the trace files. Thus, the QoEoptimization system 110 will be able to identify the node or nodes wherethe root cause of the problems is occurring and then implement remedialactions accordingly.

In various embodiments, the trace file 308 is configured to log entriesfor the data packets communicated via a node or node interface, e.g.,the traces column 312 (e.g., the trace IDs 306 in the traces column 312may correspond to multiple different client devices using the node tocommunicate). Moreover, the trace file 308 is configured to receivetiming information 314 in the form of a timestamp for each entry, andassociate/store the timestamp with the entry, as shown. Accordingly, thetrace file 308 may sequentially log a list of numerous data packet IDsand timestamps associated with when the data packets were received,transmitted, routed, and so forth.

At each node, the timestamps are logged via use of a time source (e.g.,a local time source or a remote time source). In one embodiment, thetime source may be common for the nodes, or at least some of the nodes.In an alternative, the time source may be different for each node, or atleast some of the nodes. Thus, the timing information 314 mergedtogether (from multiple trace files) may be approximated merged timinginformation because some nodes may use different time sources that maynot be synchronized.

FIG. 4 illustrates example components of the QoE optimization system110. In various embodiments, the QoE optimization system 110 may be aservice provider entity or a telecommunications provider entity that maybe part of one of the MTN nodes 106(1) . . . 106(N), or in communicationwith the MTN nodes 106(1) . . . 106(N) via a network connection.Moreover, in various embodiments, the QoE optimization system 110 may bea standalone application that is part of the client device 102 or a dataserver 108.

In various embodiments, the QoE optimization system 110 may be one ormore server or computing devices that include one or more processor(s)402 and memory 404 storing a device OS 406 and a network interfacemodule 408 that enables the trace file receiving module 410 of the QoEoptimization system 110 to communicate and receive the trace files fromthe nodes in FIG. 1A, and store the trace files or data retrieved fromthe trace files in the trace file database 412.

Each of the one or more processor(s) 402 of the QoE optimization system110 may include one or more CPUs having multiple ALUs that performarithmetic and logical operations, as well as one or more CUs thatextract instructions and content from processor cache memory, and thenexecutes the instructions by calling on the ALUs, as necessary, duringprogram execution. The processor(s) 402 may further be configured toexecute the modules stored in the memory 404.

The memory 404 may be implemented using computer readable media, such ascomputer storage media. Computer-readable media includes, at least, twotypes of computer-readable media, namely computer storage media andcommunications media. Computer storage media includes volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules, or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other non-transmission medium that can be used to storeinformation for access by a computing device. In contrast, communicationmedia may embody computer readable instructions, data structures,program modules, or other data in a modulated data signal, such as acarrier wave, or other transmission mechanism.

In various embodiments, the memory 404 may further store a trace filecorrelation module 414, a cross file analysis module 416, a controlsmodule 418, a key performance indicator (KPI) module 420, a tracesorting module 422, a presentation and notification module 424, and aremedial action module 426.

The trace file correlation module 414, describe above with respect tothe QoE analyzer 152, is configured to merge and/or otherwise correlatethe client device node trace files 112, the MTN node trace files 114(1). . . 114(N), and/or the data server node trace files 116. By mergingand/or correlating the trace files, the trace file correlation module414 matches trace IDs 306 from different nodes that may be associatedwith the same data packet. Accordingly, the trace ID 306 remainsconstant as the data packet is communicated and/or routed from theclient device 102 to the one or more data servers 108 (e.g., uplink viaa determined route/path in the MTN 104), or from the one or more dataservers 108 to the client device 102 (e.g., downlink via a determinedroute/path in the MTN 104). In at least some embodiments, the trace filecorrelation module 414 may merge or otherwise correlate a subset of atotal number of trace files collected.

In some embodiments, the trace file correlation module 414 is furtherconfigured to match corresponding request/response data packets that maynot have the same trace ID 306, but may be paired by referencing one ormore fields in the trace ID 306 that associates a response packet with arequest packet (e.g., a sequential indicator conveying that responsepacket “2” is responsive to request packet “1”). In further embodiments,the trace file correlation module 414 may match a group of data packetscommunicated within an established communication session (e.g., a videostream), by referencing one or more fields in the trace ID 306 thatassociate the data packet with the communication session. One or morefields used by the trace file correlation module 414 to match a requestpacket and a response packet, or to match data packets communicatedwithin an established communication session, may depend on a type ofcommunication protocol used.

In various embodiments, once the trace file correlation module 414merges or otherwise correlates the trace files and matches trace IDs 306for a single data packet, for a request/response packet pair, or fordata packets communicated within an established communication session,then the cross file analysis module 416 may use the correlation toperform network communications analysis and to determine the root causeof problems which may be leading to a degradation in QoE. In variousembodiments, the cross file analysis module 416 may use the timinginformation 314 for the matched trace IDs 306 to perform the networkcommunications analysis and to determine the root causes of problemsthat can be identified via timing issues. Example network communicationsanalysis may relate to: packet delay, latency mapping, packet drop rate,congestion windows, packet loss, packet error rate, location ofretransmission requests and a number of retransmission requests, etc.Moreover, results from the network communication analysis may identifyone or more nodes along the communication path that are the root causeof the problems, and why the one or more nodes are the root cause of theproblems. Therefore, the QoE optimization system 110 can identifyopportunities to optimize the QoE by eliminating the problems, or partof the problems, via remedial actions.

In various embodiments, the cross file analysis module 416 may performanalysis across the multiple correlated trace files in accordance withinstructions received from a controls module 418. The controls module418 may receive a specific type of analysis to be performed from anetwork administrator. For example, the network administrator may inputcommands to the controls module 418 that identify one or more KPIs to beanalyzed to ensure that a defined service level or service goal is or isnot being satisfied. In various embodiments, the KPI module 420 definesthe different KPIs, as listed above, for different applications 210executing on the client device 102. Moreover, the KPI module 420 mayalso define particular service levels or service goals for the KPIs(such as, e.g., the performance thresholds or models 158), as defined bya service provider or a network telecommunications provider (e.g., by anetwork administrator acting as an agent for the service provider or thenetwork telecommunications provider).

In some embodiments, the cross file analysis module 416 may performanalysis automatically. Thus, a network administrator may configure thetrace file receiving module 410 of the QoE optimization system 110 tocollect the different trace files so that they can be merged orotherwise correlated by the trace file correlation module 414 and thecross file analysis module 416 can perform some sort of analysis in aperiodic manner (every hour, every day, every two days, and so forth).In various embodiments, this automatic analysis may be performedseparately for individual KPIs or a particular combination of KPIs. Inother embodiments, the automatic and periodic analysis may be performedfor a particular application of the various applications 210 configuredto be executed on the client device 102.

In various embodiments, the trace sorting module 422 may be employed bythe cross file analysis module 416 to sort the trace IDs 306 that havebeen merged or otherwise correlated from the trace files collected. Thissorting, or filtering, may aid in the analysis performed by the crossfile analysis module 416. For example, the trace sorting module 422 mayuse one or more of the fields to sort the trace IDs so that data packetssent from or sent to a particular client device 102 are identified(e.g., a particular user or subscriber). The trace sorting module 422may use the timestamps to sort the trace IDs 306 so that data packets ina particular timing window are identified. The trace sorting module 422may use the trace sorting module 422 may use one or more of the fieldsto sort the trace IDs 306 so that data packets from a particular type ofequipment (e.g., a model from a manufacturer) are identified. The tracesorting module 422 may use one or more of the fields to sort the traceIDs 306 so that data packets communicated for a particular applicationare identified. The trace sorting module 422 may use one or more of thefields to sort the trace IDs 306 so that data packets communicatedto/from a particular source are identified (e.g., a data server 108).

In various embodiments, the QoE optimization system 110 employs thepresentation and notification module 424 to format and present anotification or alert (e.g., via a graphical user interface), such asthe alert 164, after the cross file analysis module 416 performs anetwork performance analysis. In one embodiment, a notification maystate that networks communications are well and that one or more KPIsand service levels are being satisfied. Therefore, QoE is not currentlydegraded. In an alternative embodiment, an alert may report that networkcommunications are causing degradation in QoE because one or more KPIsand a particular service level are not being satisfied. In thisalternative embodiment, the presentation and notification module 424 mayconvey a location (e.g., one or more nodes) of the root cause of theproblems and/or one or more reasons for the problems.

In some embodiments, the presentation and notification module 424 mayalso be configured to generate graphic representations 160 or textualrepresentations 162, as is described in greater detail herein. Also, thepresentation and notification module 424 may enable a user of the QoEoptimization system 110 to initiate a test communication of data packetsfrom one of the client device 102, MTN node 106, or data server 108 toanother of the client device 102, MTN node 106, or data server 108. Dataassociated with that test communication will then be represented in someor all of the trace files 112-116 and available for collection andanalysis.

In various embodiments, the remedial action module 426 may includeinstructions to remediate the network communication problems identified.Thus, the cross file analysis module and/or the presentation andnotification module 424 may access the remedial action module todetermine one or more suggested solutions to the problems, and thenpresent the selected solutions via a graphical user interface so theymay be implemented. In at least one embodiment, the remedial actionmodule 426 is configured to implement the solutions automatically inresponse to the identification of the problems.

FIG. 5 illustrates an example timing diagram 500 of data packets beingexchanged between a first node 502 (e.g., the client device 102 or UE)and a fourth node 504 (e.g., a data server 108), via a second node 506(e.g., an RNC) and third node 508 (e.g., a core network node) that maybe part of the MTN 104. This example is provided to show how the QoEoptimization system 110 may identify network communication problemsusing the timing information 314 in the trace files 308. Accordingly,the first node 502 logs trace entries in the client node trace files112, the second node 506 logs trace entries in MTN node trace files114(1), the third node 508 logs trace entries in MTN node trace files114(2), and the fourth node logs trace entries in server node tracefiles 116. While four nodes are depicted in FIG. 5, it is understood inthe context of this document that additional nodes may be involved inthe exchange of data packets between a client device 102 and a dataserver 108, particularly additional nodes within the MTN 104. Theexample timing diagram 500 in FIG. 5 represents a horizontal correlationof packets communicated across multiple nodes of a network. Horizontalcorrelation may use horizontal unique trace IDs based on packet headerinformation to correlate the packets across the multiple nodes. Incontrast, vertical correlation refers to packets as they arecommunicated amongst multiple different layers (e.g., OSI model layersor stacks) at a single node, as further discussed with respect to FIG.6. Vertical correlation may use a vertical unique trace ID based on IPpayloads to correlate the packets as they are communicated through thelayers.

FIG. 5 illustrates an initial data packet being sent from the first node502 to the fourth node 504 (e.g., via an uplink), and a response datapacket being sent from the fourth node 504 to the first node 502 (e.g.,via a downlink). Accordingly, FIG. 5 shows a RTT 510 at the first node502 that represents a time between the transmission of the initial datapacket and the reception of the response data packet.

As illustrated in FIG. 5, the initial data packet is generated at thefirst node 502 and transmitted 512 to the second node 506. Thus, thefirst node 502 may log an entry for the data packet in the client nodetrace files 112 with a timestamp (e.g., labeled “1” in FIG. 5). Thesecond node 506 receives the initial data packet, may access, changeand/or add routing information, and then relays 514 the initial datapacket to the third node 508. In association with this functionality,the second node 506 may log an entry with a timestamp for the datapacket in the MTN node trace files 114(1) (e.g., labeled “2” in FIG. 5).Similarly, the third node 508 receives the relayed data packet, mayaccess, change and/or add routing information, and then relays 516 thedata packet to the fourth node 504. Here, the third node 508 may log anentry with a timestamp for the data packet in the MTN node trace files114(2) (e.g., labeled “3” in FIG. 5).

Then the fourth node 504 receives the initial data packet and generatesand transmits 518 the response packet, logging an entry with a timestampfor the data packet received, and/or the response data packet responsetransmitted, in the server node trace files 116 (e.g., labeled “4” inFIG. 5). Similar to the uplink, the third node 508 and the second node506 route and relay the response packet back to the first node 502 at520 and 522, and log entries with timestamps for the response packet(e.g., labeled “5” and “6”). The first node 502 then logs an entry witha timestamp for the response packet (e.g., labeled “7” in FIG. 5), andthe RTT is complete.

When the QoE optimization system 110 collects the trace files associatedwith the example timing diagram in FIG. 5, the QoE optimization system110 may determine that the RTT 510 is longer than normal or longer thanexpected for the particular application being used at the first node502. After this determination, the QoE optimization system 110 mayutilize the merged trace files and the separate timestamps, as discussedabove with respect to FIG. 4, to calculate individual packetcommunication delays between the nodes (whether uplink or downlink), andidentify one or more nodes that may contribute most to the longer thanexpected RTT during the uplink and/or the downlink (e.g., at which nodewas the data packet delayed).

In various embodiments, the timing diagram 500 of FIG. 5 may berepresentative of a TCP handshake (e.g., a synchronize request and anacknowledgement response) between a client device 102 and a data server108. In other embodiments, the timing diagram 500 of FIG. 5 may berepresentative of a DNS lookup between a client device 102 and a DNSserver. In even further embodiments, the timing diagram 500 of FIG. 5may be representative of an HTTP request and a data packet responsebetween a client device 102 and a data server 108.

FIG. 6 illustrates an example of the vertical correlation 600 thatrepresents packets as they are generated at and/or communicated amongstmultiple different layers (e.g., 1 . . . N) of a communication protocolstack, such as communication protocol stack 222, at a single node. Forexample, the different layers may be associated with an OSI model andthus may be a physical layer, a data link layer, a network layer, atransport layer, a session layer, a presentation layer, and anapplication layer (as well as sublayers within the layers). Moreover,vertical correlation may use a vertical unique trace ID based on IPpayloads to correlate the packets as they are communicated through thelayers. Such vertical correlation is described above in greater detailwith reference to FIG. 1B.

FIGS. 7-11 present illustrative processes. Each process is illustratedas a collection of blocks in a logical flow chart, which represents asequence of operations that can be implemented in hardware, software, ora combination thereof. In the context of software, the blocks representcomputer-executable instructions that, when executed by one or moreprocessors, perform the recited operations. Generally,computer-executable instructions may include routines, programs,objects, components, data structures, and the like that performparticular functions or implement particular abstract data types. Theorder in which the operations are described is not intended to beconstrued as a limitation, and any number of the described blocks can becombined in any order and/or in parallel to implement the process. Fordiscussion purposes, the processes in FIGS. 7-11 are described withreference to the example environment 100 of FIG. 1A, the examplearchitecture of FIG. 1B, the example components of FIGS. 2 and 4, theexample data packet of FIG. 3A, the example trace file of FIG. 3B, theexample timing diagram of FIG. 5, and/or the example verticalcorrelation of FIG. 6.

FIG. 7 shows a flow diagram of an example process 700 for loggingentries in a trace file. The example process 700 may be performed at anode that generates, communicates, receives, transmits, routes, relays,and/or stores a data packet (e.g., the client device 102, the MTN nodes106(1) . . . 106(N), the data serves 108).

At block 702, a node monitors data packets that have been generated by,communicated through, received at, transmitted by, routed by, relayedby, and/or stored at the node. In various embodiments the monitoring maybe at the node level (e.g., a single trace file for the node) or thelayer level (e.g., multiple trace files for the node), as discussedabove.

At block 704, the node creates and logs one or more entries for themonitored data packets in a trace file 306. As discussed above, eachentry may include one or more fields that represent a trace ID 306 thatdistinguishes the data packet from other data packets. In variousembodiments, the node may log separate entries for the data packet indifferent trace files associated with different layers for the node.Alternatively, the node may log separate entries for the data packetassociated with different layers in a single trace file for the node.

At block 706, the node timestamps each trace ID 306 when logging theentry in the trace file 306. Accordingly, the node may access a timesource to determine the timing information for each entry.

At block 708, the node sends the one or more trace files to the QoEoptimization system 110. In various embodiments, the node may send thetrace files to the QoE optimization system 110 in response to a request(e.g., periodic request or on-demand request) from the QoE optimizationsystem 110. In an alternative embodiment, the node may be aware or areporting schedule, and proactively send the trace files to the QoEoptimization system 110 in accordance with the reporting schedule.

FIG. 8 shows a flow diagram of an example process 800 for collectingtrace files, merging the trace files, and performing networkcommunications analysis. The example process 800 may be performed by thecomponents that are part of the QoE optimization system 110.

At block 802, the trace file receiving module 410 may automaticallycollect the trace files from multiple nodes (e.g., the client device102, the MTN nodes 106(1), and the data servers 108). In variousembodiments, the trace file receiving module 410 may automaticallycollect the trace files in accordance with a periodic schedule. Invarious embodiments, the trace file receiving module 410 mayautomatically collect the trace files from an identified subset of nodesin the MTN 104.

At block 804, the trace file correlation module 414 merges the tracefiles collected. In various embodiments, the merging may include mergingtrace files corresponding to different layers at a single node (e.g.,layer level), as well as merging trace files received from differentnodes (e.g., node level).

At block 806, the cross file analysis module 416 analyzes the mergedtrace files to determine whether the QoE for users of client devices hasdegraded to a predefined level. In various embodiments, the cross fileanalysis module 416 performs analysis using timestamps of trace IDs thatmatch a single data packet, a request/response packet pair, a group ofdata packets that are part of an established communication session.Moreover, as part of the analysis, the cross file analysis module 416may identify (e.g., via the KPI module 420 and/or the controls module418) one or more KPIs to evaluate and a particular service level orservice goals associated with the KPI. The QoE may be found to bedegraded to the predefined level if the particular service level is notbeing satisfied (e.g., webpage loading time is longer than two seconds,RTT is greater than one second, etc.). As part of the analysis, thecross file analysis module 416 may employ the trace sorting module 422to sort the merged trace IDs so the analysis can be performed.

At block 808, the cross file analysis module 416 identifies one or morenodes and/or one or more layers within the identified nodes that may bethe root cause of the problems contributing to the degraded QoE.

At block 810, the presentation and notification module 424 may formatand generate a report or an alert to be conveyed via a GUI to a networkadministrator. The report or the alert may provide a result of the crosstrace file analysis.

At block 812, the remedial action module 426 may implement remedialactions to address the problems contributing to the degraded QoE. Invarious embodiments, the remedial actions may be implementedautomatically in accordance with predefined instructions in the controlsmodule 418. In other embodiments, the remedial actions may beimplemented in response to a selection and input provided to thecontrols module 418 by a network administrator.

FIG. 9 shows a flow diagram of another example process 900 forcollecting trace files, merging the trace files, and performing networkcommunications analysis. The example process 900 may be performed by thecomponents that are part of the QoE optimization system 110.

At block 902, the controls module 418 may receive a request from anetwork administrator to collect trace files from multiple differentnodes for cross trace file analysis.

At block 904, the trace file receiving module 410 may collect the tracefiles from multiple nodes (e.g., the client device 102, the MTN nodes106(1), and the data servers 108).

At block 906, the trace file correlation module 414 merges the tracefiles collected. In various embodiments, the merging may include mergingtrace files corresponding to different layers at a single node (e.g.,layer level), as well as merging trace files received from differentnodes (e.g., node level).

At block 908, the cross file analysis module 416 may identify one ormore trace IDs that provide a basis for the cross trace file analysisbeing requested.

At block 910, the cross file analysis module 416 may determine, based onthe identified trace IDs, whether KPIs associated with the requestedcross trace file analysis are satisfying a defined level.

At block 912, the presentation and notification module 424 may formatand the results to a network administrator requesting the analysis.

At block 914, the remedial action module 426 may implement remedialactions to address the problems.

FIG. 10 shows a flow diagram of another example process 1000 forreceiving a trace file, determining performance metrics for dataincluded in the trace file, and generating graphic or textualrepresentations of the performance metrics. The example process 1000 maybe performed by the components that are part of the QoE optimizationsystem 110.

At block 1002, the QoE optimization system 110 may receive a trace filefrom a device engaged in wireless communication. The trace file mayinclude at least data associated with a radio layer of a communicationprotocol stack of the device. The device may be one of a user device, atelecommunications base station, a wireless access point, a radionetwork controller, or a core telecommunications network element. Thetrace file may be associated with a data collection and diagnosticlogging tool for measuring radio frequency performance. In someembodiments, the trace file may also include data associated with anInternet layer, a network layer, or a transport layer of thecommunication protocol stack of the device. Alternatively, the QoEoptimization system 110 may receive, at 1002, another trace file fromthe device, and the other trace file may include the data associatedwith the Internet layer, the network layer, or the transport layer ofthe communication protocol stack of the device.

At 1004, the QoE optimization system 110 may determine, for the device,one or more performance metrics associated with radio layer keyperformance indicators based at least in part on the data associatedwith the radio layer. The radio layer key performance indicators mayinclude at least one of radio link control (RLC) retransmissions, packetloss, network signaling, radio resource control (RRC) state duration,radio state transition times, times spent in different radio states,number of radio state transitions, or reconfiguration response times.Also at 1004, the QoE optimization system 110 may determine, for thedevice, one or more additional performance metrics associated with keyperformance indicators for the Internet layer, the network layer, or thetransport layer based at least in part on the data associated with theInternet layer, the network layer, or the transport layer. The keyperformance indicators for the Internet layer, the network layer, or thetransport layer may include at least one of domain name service (DNS)round trip times (RTT), transmission control protocol (TCP) RTT,hypertext transfer protocol (HTTP) RTT, TCP retransmissions, TCPduplicate acknowledgements, TCP resets, TCP failures, delta frames, orsequence numbers.

At 1006, the QoE optimization system 110 may generate one or moregraphic or textual representations of the one or more performancemetrics. The graphic or textual representations include at least one ofa graph, a chart, or a log representation (see, for example, FIGS. 12and 13). Also, at 1006, the QoE optimization system 110 may generate oneor more additional graphic or textual representations of the one or moreadditional performance metrics.

At 1008, the QoE optimization system 110 may analyze the data based onone or more of performance thresholds or performance models. At 1010,based on the analyzing, the QoE optimization system 110 may determinethat the wireless communication exhibits a reduced QoE.

FIG. 11 shows a flow diagram of another example process 1100 forreceiving trace file(s), correlating trace file data associated withdifferent layers of a device or with different devices, analyzing thecorrelated data based on thresholds or models, and determining thatcommunication associated with the correlated data exhibits a reducedQoE. The example process 1100 may be performed by the components thatare part of the QoE optimization system 110.

At block 1102, the QoE optimization system 110 may receive a trace filefrom a device engaged in wireless packet-based communication. The tracefile may include first data for a first layer of a communicationprotocol stack of the device and second data for a second layer of thecommunication protocol stack. The wireless packet-based communicationmay comprise data packets received at a user device from a remoteservice or remote website.

At 1104, the QoE optimization system 110 may correlate the first datawith the second data based on a payload of a packet that is representedby the first data and the second data. The correlating may comprisecorrelating a representation of the payload in the first data with arepresentation of the payload in the second data.

At 1106, when multiple trace files are received from multiple devicesengaged in or relaying the wireless packet-based communication, the QoEoptimization system 110 may correlate those trace files.

At 1108, the QoE optimization system 110 may analyze the correlated databased on one or more of communication performance thresholds orcommunication performance models. If multiple trace files arecorrelated, the QoE optimization system 110 may also analyze thecorrelated trace files.

At 1110, based on the analyzing, the QoE optimization system 110 maydetermine that the wireless packet-based communication exhibits areduced QoE.

At 1112, the QoE optimization system 110 may generate a graphic ortextual representation of the correlated data. Alternatively oradditionally, at 1114, the QoE optimization system 110 may provide analert when the wireless packet-based communication exhibits a reducedQoE.

CONCLUSION

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claims.

1. A method comprising: receiving a first trace file from a deviceengaged in wireless communication, the first trace file including atleast data associated with a radio layer of a communication protocolstack of the device; receiving a second trace file, the second tracefile comprising other data for a different layer than the radio layer ofthe communication protocol stack of the device; correlating the datawith the other data based on a payload of a packet, the packetrepresented by the data and the other data; and determining one or moreperformance metrics associated with key performance indicators for thedevice based at least in part on the data and the other data.
 2. Themethod of claim 1, wherein the key performance indicators include radiolayer key performance indicators, which include at least one of radiolink control (RLC) retransmissions, packet loss, network signaling,radio resource control (RRC) state duration, radio state transitiontimes, times spent in different radio states, number of radio statetransitions, or reconfiguration response times.
 3. The method of claim1, wherein at least one of the first trace file or the second trace fileis associated with a data collection and diagnostic logging tool formeasuring radio frequency performance.
 4. The method of claim 1, whereinthe different layer is one of an Internet layer, a network layer, or atransport layer of the communication protocol stack of the device. 5.The method of claim 4, wherein the key performance indicators includeindicators for the one of the Internet layer, the network layer, or thetransport layer based at least in part on the data associated with theone of the Internet layer, the network layer, or the transport layer. 6.The method of claim 5, wherein the key performance indicators for theInternet layer, the network layer, or the transport layer include atleast one of domain name service (DNS) round trip times (RTT),transmission control protocol (TCP) RTT, hypertext transfer protocol(HTTP) RTT, TCP retransmissions, TCP duplicate acknowledgements, TCPresets, TCP failures, delta frames, or sequence numbers.
 7. The methodof claim 1, further comprising generating one or more graphic or textualrepresentations of the one more performance metrics, wherein the graphicor textual representations include at least one of a graph, a chart, ora log representation.
 8. The method of claim 1, further comprising:analyzing the data and the other data based on one or more ofperformance thresholds or performance models; and based on theanalyzing, determining that the wireless communication exhibits areduced quality of user experience (QoE).
 9. The method of claim 1,wherein the device is one of a user device, a telecommunications basestation, a wireless access point, a radio network controller, or a coretelecommunications network element.
 10. One or more non-transitorycomputer-readable media having computer-executable instructions storedthereon that, when executed by a computing device, cause the computingdevice to perform operations comprising: receiving a first trace filefrom a device engaged in wireless packet-based communication, the tracefile including first data for a first layer of a communication protocolstack of the device; receiving a second trace file including second datafor a second layer of the communication protocol stack, the second databeing from a different layer than the first layer of the communicationprotocol stack; correlating, as correlated data, the first data with thesecond data based on a payload of a packet, the packet represented bythe first data and the second data; and based on the analyzing,determining that the wireless packet-based communication exhibits areduced quality of user experience (QoE) based on one or more ofcommunication performance thresholds or communication performancemodels.
 11. The one or more non-transitory computer-readable media ofclaim 10, wherein the operations further comprise: generating a graphicor textual representation of the correlated data; or providing an alertwhen the wireless packet-based communication exhibits a reduced QoE. 12.The one or more non-transitory computer-readable media of claim 10,wherein the correlating based on the payload of the packet comprisescorrelating a representation of the payload in the first data with arepresentation of the payload in the second data.
 13. The one or morenon-transitory computer-readable media of claim 10, wherein theoperations further comprise correlating trace files of multiple devicesengaged in or relaying the wireless packet-based communication.
 14. Theone or more non-transitory computer-readable media of claim 13, whereinthe operations further comprise performing the analyzing on both thecorrelated data and based on the correlating the trace files.
 15. Asystem comprising: a trace file receiving module configured to receiveat least one trace file from each of a plurality of devices involved incommunicating data packets, each trace file including data that is forthe data packets and is associated with at least one different layer ofa multi-layer communication protocol stack of a corresponding device; atrace file correlation module configured to: correlate, as correlateddata packets, the data packets horizontally across the trace files forthe plurality of devices, and for each having two or more trace filesthat include data for multiple layers of the multi-layer communicationprotocol stack of the device, correlate the data packets verticallybetween the layers; and a cross file analysis module configured toanalyze the correlated data packets to identify one or more of theplurality of devices causing a problem that degrades a QoE associatedwith communicating the data packets.
 16. The system of claim 15, furthercomprising a presentation and notification module to: generate a graphicor textual representation of the correlated data packets; or providingan alert when a problem that degrades the QoE associated withcommunicating the data packets is identified.
 17. The system of claim16, wherein the presentation and notification module further enables auser of the system to initiate a test communication of data packets. 18.The system of claim 15, wherein the trace file correlation modulecorrelates the data packets horizontally based on trace identifiersassociated with trace entries from the trace files.
 19. The system ofclaim 15, wherein multiple trace files are collected from at least onenode, each of the multiple trace files being associated with a layer ofa communication protocol stack of the at least one node.
 20. The systemof claim 15, wherein the communicated data packets are received at auser device from a remote service or remote website.